Hyperbolic ZSL

This is the PyTorch implementation of paper: Hyperbolic Visual Embedding Learning for Zero-Shot Recognition (CVPR 2020)

Using Our Code

git clone git@github.com:ShaoTengLiu/Hyperbolic_ZSL.git
cd Hyperbolic_ZSL/code

Hyperbolic_ZSL/code is the default root directory.

Environment Requirements

anaconda is recommended
PyTorch version 1.0 or higher
At least one GPU with adequate memory
python 3.7 (python 2 may be possible with modifications)

Use environment.yml for quick start.

conda env create -f environment.yml

Data Preparation

Generate data and put them in these folders:

../data/train/img: pretrained feature from ResNet of ImageNet
../data/val/img: pretrained feature from ResNet of ImageNet
../data/train/label: pretrained word embedding from chosen word model
../data/val/label: pretrained word embedding from chosen word model

Quick Start

One can run our code via

CUDA_VISIBLE_DEVICES=1 python devise_hype.py \
    --word_model poincare \
    --model_folder model_path \
    --loss_path loss_path \
    --dimension 300

model_path: the folder to save models
loss_path: the .jpg tp save loss curve

To run DeViSE in Euclidean space via

CUDA_VISIBLE_DEVICES=2 python devise.py \
    --word_model glove \
    --model_folder model_path \
    --loss_path loss_path \
    --dimension 600

Acknowledgement

Tools for computation in Riemannian geometry are not developed by us. One can extend his model to hyperbolic space with these tools. Thanks to these good repos.

hyrnn can be found in hyrnn
geoopt can be found in geoopt
poincare_glove can be found in poincare_glove
poincare-embeddings can be found in poincare-embeddings

Code refactoring is still continuing